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memory

Persist important outcomes from this step to long-term storage with automatic forgetting curve.

53

Quality

59%

Does it follow best practices?

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SecuritybySnyk

Passed

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tessl review fix ./agentsociety/packages/agentsociety2/agentsociety2/agent/skills/memory/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

61%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The content is well-structured, actionable, and uses progressive disclosure with a real reference file and script, but it is held back by some over-explanation of known memory concepts and by the absence of validation/feedback loops around its destructive batch maintenance operation.

Suggestions

Add a validation/feedback step to the maintenance workflow (e.g. a --dry-run flag or 're-run if entries_after is unexpected; restore from backup') to lift workflow clarity past the destructive-operation cap.

Trim conceptual exposition of Ebbinghaus/ACT-R/retrieval practice in the body, pointing to references/research_basis.md for the rationale instead.

Make the write example concrete by showing the read-then-append pattern with a real joined string rather than a '<existing content>\n<new JSON line>' placeholder.

DimensionReasoningScore

Conciseness

The body is mostly efficient and well-sectioned, but it explains domain design concepts (Ebbinghaus retention, ACT-R base-level activation, retrieval practice) and includes a few editorializing lines that a capable model does not need, fitting 'mostly efficient but includes some unnecessary explanation'.

3 / 5

Actionability

It provides a concrete JSON entry schema, an executable maintenance command with flags and env vars, and a workspace_write call example; the write example uses a placeholder for existing content rather than being fully copy-paste ready, leaving a minor gap.

4 / 5

Workflow Clarity

The 'How to Write' section sequences read-then-overwrite and flags the overwrite pitfall, but the destructive/batch maintenance script overwrites memory.jsonl (dropping retention<0.1 entries) with no validation, dry-run, or fix-and-retry feedback loop, so the destructive-operation cap of 3 applies.

3 / 5

Progressive Disclosure

The body is a clear overview with a well-signaled one-level-deep reference (references/research_basis.md, a real file) and a maintenance script path, with section headers and bulk derivation detail offloaded to the reference; minor formula inlining keeps it just short of a 5.

4 / 5

Total

14

/

20

Passed

Description

57%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

The description states a clear, distinctive purpose but omits any explicit 'Use when...' trigger guidance and relies on technical phrasing rather than natural user keywords. It is competent but mid-tier because completeness and trigger-term quality are capped by the missing usage clause.

Suggestions

Add an explicit 'Use when...' clause naming natural triggers, e.g. 'Use when you need to remember, recall, or persist important outcomes across steps.'

Surface natural user keywords like 'memory', 'remember', and 'recall' alongside the technical 'long-term storage' and 'forgetting curve'.

List 1-2 more concrete actions (e.g. 'append entries to state/memory.jsonl and decay old ones') to lift specificity from mid to comprehensive.

DimensionReasoningScore

Specificity

The description names the domain and 1-2 concrete actions ('Persist important outcomes... to long-term storage with automatic forgetting curve') but does not list several specific actions, so it matches the anchor for naming a domain with minimal/generic actions rather than comprehensive coverage.

3 / 5

Completeness

It gives a clear 'what' (persist outcomes to long-term storage with automatic forgetting) but has no 'Use when...' clause, which per the guidelines caps completeness at 3 even though the 'what' is explicit.

3 / 5

Trigger Term Quality

It includes some relevant keywords ('long-term storage', 'forgetting curve', 'important outcomes') but leans technical and misses the natural phrases a user would say ('memory', 'remember', 'recall'), placing it at 'some relevant keywords but missing common variations'.

3 / 5

Distinctiveness Conflict Risk

It carves a clear niche (long-term memory with automatic forgetting curve) with distinct triggers and minimal overlap risk with other skills, matching the 'clear niche with distinct triggers' anchor.

5 / 5

Total

14

/

20

Passed

Validation

100%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
XiaoLuoLYG/GOD
Reviewed

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